Atomic Representation based Emotion Recognition from Uyghur Speech

Aiming at the problem of single restrictive condition in existing speech emotion computing algorithms based on representation learning, and that few results are reported to justify their effectiveness. A speech emotion recognition algorithm based on atomic representation model is proposed. By introducing a new condition, called atomic classification condition. In such a condition, recognition capacity to new emotion samples was improved. The existing representationbased classification algorithms are mainly sparse representation methods. The proposed algorithm can combine sparse representation model with other representation models. The algorithm can relax the scope of application conditions and adapt the representation model to more classification tasks. An Uyghur emotional speech database was established.Based on the Uyghur speech emotion database, the basic acoustic characteristics of Uyghur emotional speech are analyzed. Emotional feature space can be effectively represented by mapping of atomic representation. The experimental results show that the proposed method is better than the traditional method, and the recognition rate reached 64.17% on the Uyghur emotional speech database.